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Item Cultural Adaptation of the Bayley Scales of Infant and Toddler Development, 3rd Edition for use in Kenyan Children Aged 18–36 Months: A Psychometric Study(Elsevier, 2021) McHenry, Megan S.; Oyungu, Eren; Yang, Ziyi; Hines, Abbey C.; Ombitsa, Ananda R.; Vreeman, Rachel C.; Abubakar, Amina; Monahan, Patrick O.; Pediatrics, School of MedicineBackground: The Bayley Scales of Infant and Toddler Development, 3rd Edition (Bayley-III) is frequently used in international child development research. No studies examine its psychometric properties when culturally adapted within the Kenyan context. Aims: To culturally adapt the Bayley-III for use in Kenya and evaluate its validity and reliability. Methods and procedures: Forward and backward translation, cognitive interviews, and a brief pilot of culturally adapted items were performed. This psychometric study was part of another study on children born to mothers with HIV in Eldoret, Kenya. One hundred seventy-two children aged 18-36 months were assessed for cognition, receptive/expressive communication, and fine/gross motor domains using the Bayley-III. Confirmatory factor analysis (CFA), inter-scale Pearson correlations, internal consistency, t-tests, and test-retest reliability were performed. Outcomes and results: The mean age of children was 22.8 (SD 4.5) months old; 52.7 % (n = 89) were male. CFA revealed that both two- and three-factor indices had good and comparable fit. Pearson correlations were high between fine motor and receptive communication (r >0.70). Internal consistency was very strong for all of the subtests, with Cronbach coefficient alpha scores ranging from 0.88 to 0.96. Known groups/convergent validity was confirmed with stunting and parental concern for delays. Test-retest reliability was good and did not differ substantially across groups. Conclusions and implications: The Kenyan adapted Bayley-III is a psychometrically acceptable tool to assess child development. The scaled and composite scores should not be used to define Kenyan developmental norms, but it can be useful for comparing groups within research settings.Item Delirium diagnosis defined by cluster analysis of symptoms versus diagnosis by DSM and ICD criteria: diagnostic accuracy study(BioMed Central, 2016-05-26) Sepulveda, Esteban; Franco, Jose G.; Trzepacz, Paula T.; Gaviria, Ana M.; Meagher, David J.; Palma, Jose; Viñuelas, Eva; Grau, Imma; Vilella, Elisabet; de Pablo, Joan; Department of Psychiatry, IU School of MedicineBACKGROUND: Information on validity and reliability of delirium criteria is necessary for clinicians, researchers, and further developments of DSM or ICD. We compare four DSM and ICD delirium diagnostic criteria versions, which were developed by consensus of experts, with a phenomenology-based natural diagnosis delineated using cluster analysis of delirium features in a sample with a high prevalence of dementia. We also measured inter-rater reliability of each system when applied by two evaluators from distinct disciplines. METHODS: Cross-sectional analysis of 200 consecutive patients admitted to a skilled nursing facility, independently assessed within 24-48 h after admission with the Delirium Rating Scale-Revised-98 (DRS-R98) and for DSM-III-R, DSM-IV, DSM-5, and ICD-10 criteria for delirium. Cluster analysis (CA) delineated natural delirium and nondelirium reference groups using DRS-R98 items and then diagnostic systems' performance were evaluated against the CA-defined groups using logistic regression and crosstabs for discriminant analysis (sensitivity, specificity, percentage of subjects correctly classified by each diagnostic system and their individual criteria, and performance for each system when excluding each individual criterion are reported). Kappa Index (K) was used to report inter-rater reliability for delirium diagnostic systems and their individual criteria. RESULTS: 117 (58.5 %) patients had preexisting dementia according to the Informant Questionnaire on Cognitive Decline in the Elderly. CA delineated 49 delirium subjects and 151 nondelirium. Against these CA groups, delirium diagnosis accuracy was highest using DSM-III-R (87.5 %) followed closely by DSM-IV (86.0 %), ICD-10 (85.5 %) and DSM-5 (84.5 %). ICD-10 had the highest specificity (96.0 %) but lowest sensitivity (53.1 %). DSM-III-R had the best sensitivity (81.6 %) and the best sensitivity-specificity balance. DSM-5 had the highest inter-rater reliability (K =0.73) while DSM-III-R criteria were the least reliable. CONCLUSIONS: Using our CA-defined, phenomenologically-based delirium designations as the reference standard, we found performance discordance among four diagnostic systems when tested in subjects where comorbid dementia was prevalent. The most complex diagnostic systems have higher accuracy and the newer DSM-5 have higher reliability. Our novel phenomenological approach to designing a delirium reference standard may be preferred to guide revisions of diagnostic systems in the future.Item Development and Validation of the Patient/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life (HEAL) Scale(Dove Press, 2022-12-10) Janson, Isaac A.; Bloom, Ellen M.; Hampton, Kisha C.; Riehm Meier, Emily; Rampersad, Angeli G.; Kronenberger, William G.; Psychiatry, School of MedicineIntroduction: Hydroxyurea reduces the incidence of vaso-occlusive episodes, stroke, and respiratory, cardiac, and renal damage in sickle cell disease by increasing fetal hemoglobin. However, because suboptimal adherence to hydroxyurea limits its effectiveness, understanding patient-specific barriers to hydroxyurea adherence could help improve adherence and health outcomes in patients with sickle cell disease. The aim of this single-site, prospective, IRB-approved study was to validate a 24-item patient- and caregiver-reported hydroxyurea treatment adherence questionnaire, the Hydroxyurea Evaluation of Adherence for Life (HEAL) scale. Methods: A sample of 24 adults with sickle cell disease and 16 caregivers of children with sickle cell disease completed the HEAL scale, and a subset of the original sample provided a second HEAL scale for test-retest reliability. HEAL scale results were validated against global adherence ratings from participants and health-care providers, records of access to pill bottles, and laboratory values for fetal hemoglobin and absolute neutrophil count. Results and discussion: Results demonstrated excellent internal consistency for the HEAL Total score and eight (3-item) subscale scores (Dose, Remember, Plan, Cost, Understand, Effectiveness, Laboratory, and Pharmacy), as well as strong test-retest reliability for all HEAL scores except the Cost subscale. HEAL Total scores correlated significantly with validity measures, including global adherence ratings and lab values. The HEAL scale offers significant clinical potential for understanding adherence in individual sickle cell disease patients and significant research potential for characterizing adherence in persons with sickle cell disease who are treated with hydroxyurea.Item Envelope Method for Time- and Space-Dependent Reliability Prediction(ASCE-ASME, 2022-12) Wu, Hao; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and TechnologyReliability can be predicted by a limit-state function, which may vary with time and space. This work extends the envelope method for a time-dependent limit-state function to a time- and space-dependent limit-state function. The proposed method uses the envelope function of time- and space-dependent limit-state function. It at first searches for the most probable point (MPP) of the envelope function using the sequential efficient global optimization in the domain of the space and time under consideration. Then the envelope function is approximated by a quadratic function at the MPP for which analytic gradient and Hessian matrix of the envelope function are derived. Subsequently, the second-order saddlepoint approximation method is employed to estimate the probability of failure. Three examples demonstrate the effectiveness of the proposed method. The method can efficiently produce an accurate reliability prediction when the MPP is within the domain of the space and time under consideration.Item Heritability estimation of reliable connectome features(2018) Xie, Linhui; Salama, Paul; Shen, Li; Yan, Jingwen; Rizkalla, Maher; Ben Miled, ZinaBrain imaging genetics is an emerging research field aimed at studying the underlying genetic architecture of brain structure and function by utilizing different imaging modalities. However, not all the changes in the brain are a direct result of the genetic effect. Furthermore, the imaging phenotypes are promising for genetic analyses are usually unknown. In this thesis, we focus on identifying highly heritable measures of structural brain networks derived from Diffusion Weighted Magnetic Resonance imaging data. Using data for twins that is made available by the Human Connectome Project (HCP), the reliability of edge-level measures, namely fractional anisotropy, fiber length, and fiber number in the structural connectome, as well as seven network-level measures, specifically assortativity coefficient, local efficiency, modularity, transitivity, cluster coefficient, global efficiency, and characteristic path length, were evaluated using intraclass correlation coefficients. In addition, estimates of the heritability of the reliable measures were also obtained. It was observed that across all 64,620 network edges between 360 brain regions in the Glasser parcellation, approximately 5% were significantly high heritability based on fractional anisotropy, fiber length, or fiber number. Moreover, all tested network level measures, that capture network integrity, segregation, or resilience, were found to be highly heritable, having a variance ranging from 59% to 77% that is attributable to an additive genetic effect.Item Heritability Estimation of Reliable Connectomic Features*(Springer Nature, 2018-09) Xie, Linhui; Amico, Enrico; Salama, Paul; Wu, Yu-chien; Fang, Shiaofen; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Yan, Jingwen; Shen, Li; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.Item Managing trust and reliability for indoor tracking systems(2016) Rybarczyk, Ryan Thomas; Raje, RajeevIndoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems.Item Markov Additive Processes for Degradation with Jumps under Dynamic Environments(National Science Foundation, 2021) Shu, Yin; Feng, Qianmei; Kao, Edward P. C.; Coit, David W.; Liu, Hao; Biostatistics and Health Data Science, School of MedicineWe use general Markov additive processes (Markov modulated Lévy processes) to integrally handle the complexity of degradation including internally- and externally-induced stochastic properties with complex jump mechanisms. The background component of the Markov additive process is a Markov chain defined on a finite state space; the additive component evolves as a Lévy subordinator under a certain background state, and may have instantaneous nonnegative jumps occurring at the time the background state switches. We derive the Fokker-Planck equations for such Markov modulated processes, based on which we derive Laplace expressions for reliability function and lifetime moments, represented by the infinitesimal generator matrices of Markov chain and the Lévy measure of Lévy subordinator. The superiority of our models is their flexibility in modeling degradation data with jumps under dynamic environments. Numerical experiments are used to demonstrate that our general models perform well.Item Recovering Missing Component Dependence for System Reliability Prediction via Synergy between Physics and Data(American Society of Mechanical Engineers, 2021) Li, Huiru; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and TechnologyPredicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and therefore, the component states are assumed independent by the traditional method, which can result in a large error. This study proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density function (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.Item Reliability and Measurement Error in the Presence of Homogeneity(For the final version of the article published in the print edition of the journal as cited above, please click on the following doi link: [LINK]http://dx.doi.org/10.1300/J079v24n01_07[/LINK].[BREAK]Access to the original article may require subscription and authorized logon ID/password. IUPUI faculty/staff/students please check University Library resources before purchasing an article. Questions on finding the original article via our databases? Ask a librarian: [LINK]http://www.ulib.iupui.edu/research/askalibrarian[/LINK]., 1998) Pike, Cathy King; Hudson, Walter W.This paper describes a limitation of using Cronbach's Alpha to estimate reliability when using a sample with homogeneous responses in the measured construct. More specifically, it describes the risk of falsely concluding that a new instrument may have poor reliability and demonstrates the use of an alternate statistic that may serve as a cushion against such errors. Data from two validation studies are used to illustrate the utility of the new statistic, referred to as R-Alpha or Relative Alpha. Included is a discussion of the limitations and appropriate use of the statistic in validating multi-item tests, assessment scales, and inventories.